Method and apparatus for localization of position data

ABSTRACT

Methods, systems, apparatuses, and computer program products are provided that are configured to perform localization of position data, specifically using a trained localization neural network. In the context of an apparatus, the apparatus is caused to receive observed feature representation data. The apparatus is further configured to transform the observed feature representation data into standardized feature representation data utilizing a trained localization neural network. The apparatus is further configured to compare the standardized feature representation data and the map feature representation data and identify local position data based on the comparison.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/227,945, filed on Dec. 20, 2018, titled “Method and Apparatus ForLocalization Of Position Data,” the contents of which are incorporatedherein by reference in its entirety.

TECHNOLOGICAL FIELD

Embodiments of the present invention relate generally to a method,apparatus, and computer program product for localization of positiondata, and more specifically, for localization of position data thathandles feature changes by approximating environment observations usinga neural network trained to transform input data representing theobserved environment feature to a representation that approximates theenvironment feature at a time an environment map was constructed.

BACKGROUND

Modern day vehicles utilize a plurality of sensors, for example cameras,Light Detection and Ranging (LiDAR), and radar, to accomplish varioussafety and navigation tasks. For example, some autonomous vehicles useat least LiDAR, cameras, and various other sensors to ensure navigationand control of the vehicle occurs properly and reliably. Sensors areused to capture representations of the environment including environmentfeatures (“features”). Autonomous vehicles may detect and/or interpretfeatures to perform vehicle localization. For example, an autonomousvehicle may detect a lane line to determine the vehicle's positioncorresponding to a previously generated and/or stored archivalenvironment map.

An appearance of a given feature may change over time, for example dueto weather effects, lighting effects, weathering or degradation of thefeature itself, or the like. Changes to a feature's appearance may causethe feature to be undetectable, or wrongly detected, by systems thatperform such detection in real-time. For example, an autonomous vehiclesystem, such as a perception system, may fail to accurately detectand/or interpret a lane marker when the lane marker has been worn downover time. Inaccurate detection may lead to inefficiencies, for examplethrough over-cautious behaviors. Inaccurate feature detection may alsocause incorrect decision making, causing unreliable and/or incorrectdecisions.

BRIEF SUMMARY

In general, embodiments of the present disclosure provide for localizingposition data, such as for real-time vehicle localization. Otherimplementations for localizing position data, such as for real-timevehicle localization will be, or will become, apparent to one with skillin the art upon examination of the following figures and detaileddescription. It is intended that all such additional implementations beincluded within this description be within the scope of the disclosure,and be protected by the following claims.

In accordance with a first aspect of the disclosure, acomputer-implemented method for position localization is provided. Thecomputer-implemented method may be executed via any of a myriad ofcomputing devices embodied in hardware, software, firmware, and/or acombination thereof, as described herein. In at least one exampleembodiment, an example computer-implemented method includes receivingobserved feature representation data captured by a sensor, the observedfeature representation data comprising an environment feature associatedwith an observed feature decay. The example computer-implemented methodfurther includes using a trained localization neural network, transformthe observed feature representation data to standardized featurerepresentation data, wherein the standardized feature representationdata approximates a map feature representation of the environmentfeature. The example computer-implemented method further includescomparing the standardized feature representation data to map featurerepresentation data that was captured during construction of the mapfeature representation. The example computer-implemented method furtherinclude identifying a localized position based upon the comparison.

Additionally or alternatively, in some embodiments of the examplecomputer-implemented method, the map feature representation data issubject to a feature decay at a time of capture.

Additionally or alternatively, in some embodiments of the examplecomputer-implemented method, the observed feature representation data isin a raw data format.

Additionally or alternatively, in some embodiments of the examplecomputer-implemented method, the observed feature representation data isin a pre-processed data format.

Additionally or alternatively, in some embodiments of the examplecomputer-implemented method, the example computer-implemented methodfurther includes outputting the localized position.

Additionally or alternatively, in some embodiments of the examplecomputer-implemented method, the trained localization neural network isa trained generative adversarial network.

Additionally or alternatively, in some embodiments of the examplecomputer-implemented method, an overall context of the standardizedrepresentation data corresponds to the overall context of the mapfeature representation.

In accordance with a second aspect of the disclosure, an apparatus forposition localization is provided. In one example embodiment, an exampleapparatus includes at least one processor and at least one memory havingcomputer-coded instructions stored thereon, the computer-codedinstructions configure the apparatus, upon execution via the at leastone processor, to perform any one of the example computer-implementedmethods described herein. In another example embodiment, an exampleapparatus includes means for performing each step of any one of theexample methods described herein.

In accordance with a third aspect of the disclosure, a computer programproduct for position localization is provided. In one exampleembodiment, an example computer program product includes at least onenon-transitory computer-readable storage medium having computer programcode stored thereon. The computer program code configures the computerprogram product, in execution with at least one processor, forperforming any one of the computer-implemented methods described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described certain example embodiments of the presentinvention in general terms, reference will hereinafter be made to theaccompanying drawings which are not necessarily drawn to scale, andwherein:

FIG. 1 is a block diagram of a system for position localization in whichan example embodiment of the present invention may operate;

FIG. 2 is a block diagram of an apparatus according to an exampleembodiment of the present invention;

FIG. 3 depicts feature space decay for a single, sample feature inaccordance with an example embodiment of the present invention;

FIG. 4 illustrates a visualization of an example embodiment describedherein; and

FIG. 5 illustrates a flowchart of a method in accordance with an exampleembodiment of the present invention.

DETAILED DESCRIPTION

Embodiments of the present invention now will be described more fullyhereinafter with reference to the accompanying drawings, in which some,but not all embodiments of the inventions are shown. Indeed, embodimentsof the invention may be embodied in many different forms and should notbe construed as limited to the embodiments set forth herein; rather,these embodiments are provided so that this disclosure will satisfyapplicable legal requirements. Like numbers refer to like elementsthroughout.

As used herein, the terms “data”, “content”, “information”, and similarterms may be used interchangeably to refer to data capable of beingcaptured, transmitted, received, displayed, and/or stored in accordancewith various example embodiments. Thus, use of any such terms should notbe taken to limit the spirit and scope of the disclosure. Further, wherea computing device is described herein to receive data from anothercomputing device, it will be appreciated that the data may be receiveddirectly from another computing device, or may be received indirectlyvia one or more intermediary computing devices, such as, for example,one or more servers, relays, routers, network access points, basestations, and/or the like, sometimes referred to herein as a “network.”Similarly, where a computing device is described herein to send data toanother computing device, it will be appreciated that the data may besent directly to another computing device or it may be sent indirectlyvia one or more intermediary computing devices, such as, for example,one or more servers, relays, routers, network access points, basestations, and/or the like.

Overview

Autonomous vehicle operation relies on at least one environment map. Theenvironment often includes a plurality of environment observationrepresentations (“map feature representations”) captured beforeconstructing the environment map. For example, an example environmentmap may include representations of lane markers, exit signs, streetsigns, crosswalks, and the like, captured during an initial drive by oneor more capture vehicles. These map feature representations includeenvironment features and/or markers that are helpful in determiningwhere a particular, localized position lies in the environment map.Utilizing the plurality of map feature representations, the environmentmap serves as an archival environment map for localization.

Autonomous vehicles perform vehicle localization to determine theposition of the vehicle, or group of vehicles, with respect to anenvironment map. During vehicle localization, sensor data is captured.The captured sensor data often represents observed featurerepresentation. For example, a camera sensor on an autonomous vehiclecaptures an image of the road including a lane marker. When observedfeature representations are positively compared to map featurerepresentations, the autonomous vehicle may utilize that positivecomparison to determine a localized position.

However, in a given observed feature representation, an observedfeatured may not appear similar to its appearance in a map featurerepresentation. For example, a lane marker freshly painted at the time aparticular environment map may have degraded due to road use,weathering, or other feature decay, and thus be less visible than thecorresponding version stored in the environment map. Visual changes in afeature makes directly comparing the observed feature representation(e.g., weathered and worn down) to the map feature representation (e.g.,new and freshly painted) unlikely to yield an accurate result.Accordingly, vehicle localization that relies on accurately detectingand interpreting observed feature representations to match theircorresponding map feature representations will similarly be inaccurate.

Various embodiments of the disclosure relate to vehicle localizationusing a neural network to transform an observed feature representationinto standardized feature representation data. In an example embodiment,a neural network is trained to transform observed lane markerrepresentations into a standardized representation. In an exampleembodiment, a standardized representation is associated with an overallcontext, for example associated weather conditions, an associatedfeature decay, associated lighting conditions, etc, associated with astored map feature representation. In a particular example embodiment, astandardized representation of a lane marker is associated with anoverall context of “summer-like conditions, with fresh paint.” In someembodiments, comparing the standardized feature representation and a mapfeature representation allows for identifying a localized position.

Definitions

The term “environment feature” refers to a visual indicator for use innavigating, controlling, or otherwise utilizing an autonomous vehicle.Examples of environment features include, but are not limited to, lanemarkers, sign posts, stop signs, crosswalks, yield signs, and the like.

The term “feature decay” refers to degradation of an environment featurebetween two times at which the environment feature is observed. Examplesof feature decay are fading of an environment feature due to use, fadingof an environment feature due to weather, obscuring of an environmentfeature due to time, or the like. For example, a lane marker may haveassociated feature decay indicating no degradation when the environmentfeature is fully visible (e.g., when it is freshly painted), and a lanemarker may have an associated feature decay indicating significantdegradation when the lane marker is barely visible (e.g., when paint haseroded due to time and/or inclement weather).

The term “sensor” refers to a hardware component or system configured tocapture one or more representations of an environment. Examples ofsensors include, but are not limited to, cameras, Light Detection andRanging (LiDAR) systems, radar, and the like. In some embodiments, asensor outputs representation data that represents a particularrepresentation of the environment captured by the sensor. In someembodiments, the environment feature representation includes one or moreenvironment features.

The term “observed feature representation” refers to a particularcapture of an environment by a sensor, wherein the particular captureincludes an environment feature associated with an observed featuredecay. In some example embodiments, sensors capture observed featurerepresentations in real-time. In some embodiments, an observed featurerepresentation is associated with significant feature decay, such thatthe environment feature included in the representation is barelyvisible. Data representing an observed feature representation isreferred to as “observed feature representation data.”

The term “map feature representation” refers to a particular capture ofan environment by a sensor during map construction, wherein theparticular capture includes an environment feature associated with afeature decay. In some embodiments, a map representation includes anenvironment feature that is associated with minimal feature decay. Forexample, an example map representation including a lane marker may becaptured during map construction, wherein the lane marker is freshlypainted. In some embodiments, a map representation includes anenvironment feature that is associated with significant feature decay.For example, an example map representation including a lane marker maybe captured during map construction, wherein the lane marker is barelyvisible. Data representing a map feature representation is referred toas “map feature representation data.”

The term “standardized feature representation” refers to a capturegenerated utilizing a neural network that approximates a map featurerepresentation based on an observed feature representation. In someembodiments, each of (1) the standardized feature representation, (2)the observed feature representation that the standardized featurerepresentation is based on, and (3) the map feature representation beingapproximated, each are associated with their own feature decay. In someembodiments, the map feature representation and standardized featurerepresentation are associated with the same feature decay. In someembodiments, a neural network is trained to generate a standardizedfeature representation by transforming an observed featurerepresentation, such as by applying a learned transformation function.For example, in a particular example embodiment, a trained neuralnetwork transforms an observed feature representation including a lanemarker associated with significant feature decay (e.g., a lane markerbarely visible) into a standardized feature representation including alane marker associated with minimal feature decay (e.g., a lane markerfreshly painted) that approximates a map feature representationincluding a lane marker associated with minimal feature decay (e.g., alane marker freshly painted). Data representing a standardized featurerepresentation is referred to as “standardized feature representationdata.”

Accordingly, the term “environment feature representation” refersgenerally to any map feature representation, observed featurerepresentation, or standardized feature representation.

The term “environment map” refers to a set of stored map featurerepresentations, such that the set of stored map feature representationsrepresent a real-world environment. In some embodiments, a particularfeature decay is associated with each stored environment featurerepresentation in the set of stored map feature representations. In someembodiments, feature representations stored in an environment map areaccessible for comparison with observed feature representations used invehicle localization to determine an accurate position of a vehicle. Anexample environment map is a high-definition (“HD”) map.

The term “localized position” refers to an identified position of avehicle or group of capture vehicles associated with one or moreenvironment maps. In some embodiments, a localized position isdetermined using a localization process utilizing a comparison function.For example, in some embodiments, a standardized feature representationbased on an observed feature representation is compared with a mapfeature representation stored in an environment map, such that acorresponding region in the environment map where the two are deemed amatch by the comparison function corresponds to the localized positionof a vehicle, or group of vehicles, configured to capture the observedfeature representation utilizing a particular sensor. Data representinga particular localized position is referred to as “localized positiondata.”

Technical Underpinnings and Implementations of Example Embodiments

A neural network vehicle localization apparatus identifies a localizedposition of a vehicle using a trained neural network. Some embodimentsreceive observed feature representation data, transform the observedfeature representation data into standardized feature representationdata utilizing a trained neural network, wherein the standardizedfeature representation data represents a standardized featurerepresentation that approximates a map feature representation, comparethe standardized feature representation data with map featurerepresentation data, wherein the map feature representation datarepresents the map feature representation, and identify localizedposition data, wherein the localized position data represents alocalized position in an environment map.

An environment map may store static map feature representations capturedduring map construction. These stored static map feature representationsmay be used to localize the position of a vehicle or group of vehiclesby comparing one or more observed feature representations withcorresponding map feature representations stored in the environment map.If the observed feature representations match with the corresponding mapfeature representations stored in the environment map for a particularposition, that position is the localized position of the vehicle orgroup of vehicles.

Accordingly, accurately comparing observed feature representations totheir corresponding map feature representations helps to accuratelyidentify a localized position for a vehicle or group of vehicles.Similarly, accurate results from comparison between an observed featurerepresentation and a map feature representation is also advantageous,such that a particular environment feature should be deemed a match toits corresponding map feature representation every time a vehicle is ata particular position.

However, changes in feature decay pose challenges for accuratelymatching observed feature representations to their corresponding mapfeature representation. For example, a lane marker may have been freshlypainted in a clear summer day when an environment map was constructed,and later captured in a real-time observed feature representation whenthe lane marker has been slightly eroded on a stormy winter day. Suchsignificant feature decay makes comparison between the observed featurerepresentation and the mapped feature representation likely to beinaccurate if directly compared.

By standardizing observed feature representation data, the method,apparatus, and computer program product of example embodiments of thepresent invention are configured to robustly perform comparisons. Forexample, even if an observed feature representation is associated withsevere feature decay, for example such that the environment feature isbarely visible, no re-parameterizing is required to accurately performthe comparison. Accordingly, the method, apparatus, and computer programproduct of example embodiment systems improve efficiency and efficacy ofsuch comparisons. Similarly, the method, apparatus, and computer programproduct of example embodiment systems are more robust than traditionalsystems in handling real-time changes in observed featurerepresentations from corresponding map feature representations in anenvironment map.

Additionally, the method, apparatus, and computer program product ofexample embodiment systems do not require transformation of observedfeature representations into a particular representation type, such as avector-representation. The method, apparatus, and computer programproduct of an example embodiment makes no assumptions about therepresentation type associated with a map feature representation storedin an environment map. Thus, the method, apparatus, and computer programproduct of some example embodiments assume that the environment mapcomprises a raw, registered data-dump of sensor readings. The method,apparatus, and computer program product of some embodiments areconfigured to operate when an environment map is constructed using mapfeature representations stored in processed data formats, for example apolyline representation.

System Architecture

Methods, apparatuses, and computer program products of an exampleembodiment may be embodied by any of a variety of devices. For example,the method, apparatus, and computer program product of an exampleembodiment may be embodied by a network device, such as a server orother entity, configured to communicate with one or more devices, suchas one or more vehicles, systems, or user devices. Additionally oralternatively, the method, apparatus, and computer program product of anexample embodiment may be embodied by one or more computing deviceshaving one or more software modules or otherwise being configured tocarry out all or some of the operations disclosed herein.

In this regard, FIG. 1 is a block diagram showing an example system inwhich embodiments of the present invention may operate. An examplesystem includes mobile device 102, localization system 104, and map dataservice provider 108. Each of the mobile device 102, localization system104, and map data service provider 108 may be in communication with atleast one of the other elements illustrated in FIG. 1 via a network 106,which may be in any form of wireless or partially wireless network.Additional, different, or fewer components are provided in alternativesystems. For example, a plurality of capture devices similar to themobile device 102 may connect with the network 106. The map data serviceprovider 108 may be a cloud-based service provider and/or may operatevia a hosting server that receives, processes, and provides data toother elements in the system.

Mobile device 102 may be embodied or otherwise onboard an autonomousvehicle including a plurality of sensors, such that the autonomousvehicle is configured to capture representations of a surroundingenvironment utilizing the plurality of sensors. Mobile device 102 mayalternatively be a mobile user device, such as a smart phone, or thelike, configured to perform mapping capabilities. In an example system,mobile device 102 may include, be associated with, or otherwise be incommunication with a plurality of sensors configured to capture asurrounding environment. In some embodiments, the plurality of sensorsare configured to capture observed feature representations and transmitobserved feature representation data for transformation in real-time.

The map data service provider 108 includes map database 110 thatincludes one or more stored environment maps. In some embodimentsystems, map database 110 may include node data, road segment data orlink data, point of interest (POI) data, traffic data or the like. Themap database 110 may also include cartographic data, routing data,and/or maneuvering data. According to some example embodiments, the roadsegment data records may be links or segments representing roads,streets, or paths, as may be used in calculating a route or recordedroute information for determination of one or more personalized routes.The node data may be end points corresponding to the respective links orsegments of road segment data. The road link data and the node data mayrepresent a road network, such as used by vehicles, cars, trucks, buses,motorcycles, and/or other entities. Optionally, the map database 110 maycontain path segment and node data records or other data that mayrepresent pedestrian paths or areas in addition to or instead of thevehicle road record data, for example. The road/link segments and nodescan be associated with attributes, such as geographic coordinates,street names, address ranges, speed limits, turn restrictions atintersections, and other navigation related attributes, as well as POIs,such as fueling stations, hotels, restaurants, museums, stadiums,offices, auto repair shops, buildings, stores, parks, etc. The mapdatabase 110 can include data about the POIs and their respectivelocations in the POI records. The map database 110 may include dataabout places, such as cities, towns, or other communities, and othergeographic features such as bodies of water, mountain ranges, etc. Suchplace or feature data can be part of the POI data or can be associatedwith POIs or POI data records (such as a data point used for displayingor representing a position of a city). In addition, the map database 110can include event data (e.g., traffic incidents, constructionactivities, scheduled events, unscheduled events, etc.) associated withthe POI data records or other records of the map database 110.

The map database 110 may be maintained by a content provider e.g., themap data service provider 108, and may be accessed, for example, by themap data service provider server 112. By way of example, the map dataservice provider 108 may collect geographic data and dynamic data togenerate and enhance the map database 110 and dynamic data such astraffic-related data contained therein. There can be different ways usedby the map developer to collect data. These ways can include obtainingdata from other sources, such as municipalities or respective geographicauthorities, such as via global information system databases. Inaddition, the map developer can employ field personnel to travel byvehicle along roads throughout the geographic region to observe featuresand/or record information about them, for example. Also, remote sensing,such as aerial or satellite photography and/or LiDAR, can be used togenerate map geometries directly or through machine learning asdescribed herein. However, the most ubiquitous form of data that may beavailable is vehicle data provided by one or more vehicles, such as byone or more mobile device 102, as they travel the roads throughout aregion.

The map database 110 may be a master map database, such as anhigh-definition (HD) map database, stored in a format that facilitatesupdates, maintenance, and development. For example, the master mapdatabase or data in the master map database can be in an Oracle spatialformat or other spatial format, such as for development or productionpurposes. The Oracle spatial format or development/production databasecan be compiled into a delivery format, such as a geographic data files(GDF) format. The data in the production and/or delivery formats can becompiled or further compiled to form geographic database products ordatabases, which can be used in end user navigation devices or systems.

For example, geographic data may be compiled (such as into a platformspecification format (PSF) format) to organize and/or configure the datafor performing navigation-related functions and/or services, such asroute calculation, route guidance, map display, speed calculation,distance and travel time functions, and other functions, by a navigationdevice, such as by a vehicle represented by mobile device 102, forexample. The navigation-related functions can correspond to vehiclenavigation, pedestrian navigation, or other types of navigation. Thecompilation to produce the end user databases can be performed by aparty or entity separate from the map developer. For example, a customerof the map developer, such as a navigation device developer or other enduser device developer, can perform compilation on a received mapdatabase in a delivery format to produce one or more compiled navigationdatabases.

As mentioned above, the map data service provider 108 map database 110may be a master geographic database. In alternate embodiments, a clientside map database may represent a compiled navigation database that maybe used in or with end user devices (e.g., mobile device 102) to providenavigation and/or map-related functions. For example, the map database110 may be used with the mobile device 102 to provide an end user withnavigation features. In such a case, the map database 110 can bedownloaded or stored on the end user device which can access the mapdatabase 110 through a wireless or wired connection, such as via server112 and/or the network 106 for example.

In one embodiment, as noted above, the mobile device 102 or end userdevice can embody, or otherwise be in communication or associated withthe apparatus 200 of FIG. 2, which, in turn, may be embodied by orotherwise be in communication with an Advanced Driver Assistance System(ADAS) which may include an infotainment in-vehicle system or anin-vehicle navigation system of a vehicle, such as an autonomous orsemi-autonomous vehicle, and/or devices such as a personal navigationdevice (PND), a portable navigation device, a cellular telephone, asmart phone, a personal digital assistant (PDA), a watch, a camera, acomputer, and/or other device that can perform navigation-relatedfunctions, such as digital routing and map display. An end user can usethe mobile device 102 for navigation and map functions such as guidanceand map display, for example, and for determination of useful driverassistance information, according to some example embodiments.

The map database 110 of example embodiments may be generated from aplurality of different sources of data. For example, municipalities ortransportation departments may provide map data relating to road ways,while geographic information survey systems may provide informationregarding property and ownership of property within a geographic region.Further, data may be received identifying businesses at propertylocations and information related to the businesses such as hours ofoperation, services or products provided, contact information for thebusiness, etc. Additional data may be stored in the map database such astraffic information, routing information, etc. This data may supplementthe HD map data that provides an accurate depiction of a network ofroads in the geographic region in a high level of detail including roadgeometries, features along the roads such as signs, etc. The data storedin the map database may be gathered from multiple different sources, andone source of data that may help keep the data in the map database freshis map data provided by vehicles traveling along the road segments ofthe road network.

While municipalities and businesses may provide map data to a mapdatabase, the ubiquity with which vehicles travel along road segmentsrender those vehicles as opportunities to collect data related to theroad segments provided the vehicles are equipped with some degree ofsensor technology. A vehicle traveling along a road segment with onlylocation sensing technology such as a Global Positioning System mayprovide data relating to the path of a road segment, while vehicles withmore technologically advanced sensors may be able to provide additionalinformation. Sensor data from image sensors or depth sensors such asLiDAR may provide details regarding the features of road segmentsincluding the position of signs along the road segment and theinformation contained on the signs. This data may be crowd sourced bymore than one map data service provider 108 to build more robust andreliable maps with a greater level of detail than previously available.Further, beyond building the maps in the map database 110, sensor datamay be used to update map data or confirm existing map data to ensurethe map database 110 is maintained and as up-to-date as possible. Theaccuracy and freshness of map data may be critical as vehicles becomemore advanced and autonomous control of vehicles becomes more ubiquitousas the map database 110 may provide information that facilitates controlof a vehicle along a road segment.

In an example embodiment, the map database 110 at least stores anenvironment map, such as a HD map, which includes map featurerepresentations captured at a first time, such as during construction ofthe environment map. In example systems where map database 110 stores anHD environment map that includes map feature representations captured ata first time, each map feature representation may be associated with afeature decay. The associated feature decay may be representative ofweathering, wear, or other effect applied to the environment feature.The feature decay may be associated with a particular location of thefeature (e.g., a left side of a lane marker is very worn), and/or anassociated decay level/severity (e.g., a lane marker may be freshlypainted, slightly worn, very worn, or severely/totally worn/decayed).

In some embodiments, localization system 104 is a sub-system of mobiledevice 102. However, in other embodiments, localization system 104 is asub-system of map data service provider 108. In further embodiments,localization system 104 comprises sub-modules of both mobile device 102and map data service provider 108.

The localization system may be embodied by one or more computingsystems, such as localization apparatus 200 depicted in FIG. 2. Asillustrated in FIG. 2, the apparatus 200 may include one or more of aprocessor 202, a memory 204, input/output circuitry 206, communicationscircuitry 208, a plurality of sensors 210A-210N, and neural networklocalization circuitry 212. The apparatus 200 may be configured toexecute the operations described above with respect to FIG. 1 and belowwith respect to FIGS. 4 and 5. Although these components 202-212 aredescribed with respect to the performance of various functions, itshould be understood that the particular implementations necessarilyinclude the use of particular hardware. It should also be understoodthat certain of these components 202-212 may include similar or commonhardware. For example, two modules/circuitry may both leverage use ofthe same processor, network interface, storage medium, or the like toperform their associated functions, such that duplicate hardware is notrequired for each module. The use of the terms “module” and “circuitry”as used herein with respect to components of the apparatus thereforeincludes particular hardware configured to perform the functionsassociated with the particular module described herein.

Of course, the terms “module” and “circuitry” should be understoodbroadly to include hardware. In some embodiments, circuitry may alsoinclude software for configuring the hardware. For example, in someembodiments, “module” and/or “circuitry” may include processingcircuitry, storage media, network interfaces, input/output devices, andthe like. In some embodiments, other elements of the apparatus 200 mayprovide or supplement the functionality of particular module(s). Forexample, the processor 202 may provide processing functionality, thememory 204 may provide storage functionality, the communicationscircuitry 208 may provide network interface functionality, and the like.

The processor 202 may be embodied in a number of different ways and may,for example, include one or more processing devices configured toperform independently. Additionally or alternatively, the processor mayinclude one or more processors configured in tandem via a bus to enableindependent execution of instructions, pipelining, and/ormultithreading. The use of the terms “processing module” and/or“processing circuitry” may be understood to include a single coreprocessor, a multi-core processor, multiple processors internal to theapparatus, and/or remote or “cloud” processors.

In an example embodiment, the processor 202 may be configured to executeinstructions stored in the memory 204 or otherwise accessible to theprocessor. Alternatively or additionally, the processor may beconfigured to execute hard-coded functionality. As such, whetherconfigured by hardware or software methods, or by a combination ofhardware with software, the processor may represent an entity (e.g.,physically embodied in circuitry) capable of performing operationsaccording to an embodiment of the present invention while configuredaccordingly. Alternatively, as another example, when the processor isembodied as an executor of software instructions, the instructions mayspecifically configure the processor to perform the algorithms and/oroperations described herein when the instructions are executed.

In some embodiments, the apparatus 200 may optionally includeinput/output circuitry 206 that may, in turn, be in communication withprocessor 202 to provide output to the user and, in some embodiments, toreceive an indication from the user. The input/output circuitry 206 maycomprise user interface associated with a hardware and/or softwaredisplay. In some embodiments, the input/output circuitry 206 may alsoinclude a keyboard, a mouse, a joystick, a touch screen, touch areas,soft keys, a microphone, a speaker, or other input/output mechanisms.The processor and/or user interface circuitry comprising the processormay be configured to control one or more functions of one or more userinterface elements through computer program instructions (e.g., softwareand/or firmware) stored on a memory accessible to the processor (e.g.,memory 204, and/or the like).

The communications circuitry 208 may be any means such as a device,module, or circuitry embodied in either hardware or a combination ofhardware and software that is configured to receive and/or transmit datafrom/to a network and/or any other device, circuitry, or module incommunication with the apparatus 200. In this regard, the communicationscircuitry 208 may include, for example, a network interface for enablingcommunications with a wireless communication network. For example, thecommunications circuitry 208 may include one or more network interfacecards, antennae, buses, switches, routers, modems, and supportinghardware and/or software, or any other device suitable for enablingcommunications via a network. Additionally or alternatively, thecommunication interface may include the circuitry for interacting withthe antenna(s) to cause transmission of signals via the antenna(s) or tohandle receipt of signals received via the antenna(s).

In some embodiments, apparatus 200 may include, be associated with, orin communication with, one or more sensors, such as sensors 210A-210N,designed to capture an environment including environment features.Example sensors may include a global positioning system (GPS),accelerometer, LiDAR, radar, and/or gyroscope. Any of the sensors may beused to sense information regarding the movement, positioning, ororientation of the device for use in navigation assistance, as describedherein according to example embodiments. In some example embodiments,such sensors may be implemented in a vehicle or other remote apparatus,and the information detected may be transmitted to the apparatus 200,such as by a proximity-based communication technique, such as near fieldcommunication (NFC) including, but not limited to, Bluetooth™communication, or the like. Multiple sensors of the sensors 210A-210Nmay be utilized to capture a particular environment representation. Insome embodiments, sensors 210A-210N may communicate with other apparatuscomponents, such as the neural network localization circuitry 212,input/output circuitry 206, or communications circuitry 208, such as viaprocessor 202.

Neural network localization circuitry 212 includes hardware and/orsoftware components configured to transform an observed featurerepresentation into a standardized feature representation thatapproximates a map feature representation. In some embodiments, neuralnetwork localization circuitry 212 embodies a neural network that hasbeen trained to receive observed feature representation data, such asthrough communications from sensors 210A-210N via processor 202, fortransforming. Neural network localization circuitry 212 may utilize oneor more other modules or circuitry, such as communications circuitry208, to receive and/or transmit data. For example, in an exampleembodiment, neural network localization circuitry 212 communicates withcommunications circuitry 208 via processor 202 to receive map featurerepresentation data for use in training a neural network.

Neural network localization circuitry 212 may, for instance, utilize, orbe embodied by a processing module, such as processor 202, to performthe above operations, and may further utilize any of the other modules,such as communications circuitry 208, for their purposes as describedherein to perform the above operations. It should be appreciated that,in some embodiments, the neural network localization circuitry 212 mayinclude a separate processor, specially configured field programmablegate array (FPGA), or application specific interface circuit (ASIC) toperform the above functions.

The apparatus 200 and, more specifically, the processor 202 and/or theneural network localization circuitry may operate under control ofcomputer program instructions, as may be stored by the memory 204. Aswill be appreciated, any such computer program instructions and/or othertype of code may be loaded onto a computer, processor, or otherprogrammable apparatus' circuitry, such as the processor 202 and/or theneural network localization circuitry 212, to produce a machine, suchthat the computer, processor, or other programmable circuitry, such asthe processor 202 and/or neural network localization circuitry 212, thatexecutes the coded instructions on the machine creates the means forimplementing various functions, including those described herein.

As described above and as will be appreciated based on this disclosure,embodiments of the present invention may be configured as a method,apparatus, computer program product, autonomous vehicle system, and thelike. Accordingly, embodiments may comprise various means includingentirely of hardware or any combination of software and hardware.Furthermore, embodiments may take the form of a computer program producton at least one non-transitory computer-readable storage medium havingcomputer-readable program instructions (e.g., computer software)embodied in the storage medium. Any suitable computer-readable storagemedium may be utilized including non-transitory hard disks, CD-ROMs,flash memory, optical storage devices, or magnetic storage devices.

Example System Data Flow

FIG. 3 illustrates an example embodiment of feature space decay for anexample feature, specifically exit signs. As depicted in FIG. 3, anenvironment feature representation 302 is captured by a sensor, such asan image sensor, of an environment of the sensor. In this example, theenvironment of the sensor is a roadway, and the detected feature is alane marker 308. The lane marker 308 is, or appears, fresh, new, orotherwise visible. Accordingly, environment feature representation 302may be associated with a feature decay indicating the environmentfeature, lane marker 308, is “freshly painted,” for example. However,the lane marker becomes weathered, worn, and otherwise decays over time.Environment feature representation 304 illustrates a partial decay ofthe featured lane marker, specifically lane marker 310. Accordingly,environment feature representation 304 may be associated with a featuredecay indicating the environment feature, lane marker 310, is “partiallyworn,” for example. Environment feature representation 306 illustrates asignificant decay of the featured lane marker, specifically lane marker312. Accordingly, environment feature representation 306 may beassociated with a feature decay indicating the environment feature, lanemarker 312, is “total decay”, for example. While each of the environmentfeature representations 302-306 correspond to the same location, each ofthe environment feature representations 302-306 corresponds to acaptured representation (e.g., by a sensor) at a different time, andthus a different feature decay applies to each depicted environmentfeature representation 302-306.

Each map feature representation stored in an environment may beassociated with any of the feature decays depicted in FIG. 3, or otherfeature decays not depicted. For example, an environment map may includeone or more map feature representations of lane markers that areassociated with a feature decay of “freshly painted”, as well as one ormore map feature representations of lane markers that are associatedwith a feature decay of “partially worn” or “total decay”. Similarly,observed feature representations may be associated with a feature decayof “freshly painted”, “partially worn”, or “total decay” as well.

An overall context that defines the conditions of an environment featurerepresentation may also be associated with a particular environmentfeature representation. An example overall context includes a weatherdescriptor and a feature decay associated with a particular environmentfeature representation. For example, if the environment featurerepresentations 302-306 were captured on a sunny summer day, environmentfeature representation 302 may be associated with an overall context of“sunny summer conditions, freshly painted,” environment featurerepresentation 304 may be associated with an overall context of “sunnysummer conditions, partially worn,” and environment featurerepresentation 306 may be associated with an overall context of “sunnysummer conditions, total decay.” Accordingly, an overall contextassociated with an environment feature representation may be utilized tocompare environment feature representations, or transform an observedfeature representation associated with a first overall context into astandardized feature representation associated with a second overallcontext. In some embodiments, an overall context includes a text labeldescribing the feature decay associated with a particular environmentfeature. The overall context may, in some embodiments, be human created.An overall context may be associated with a unique identifier.

Additionally, it should be appreciated that the three depictedenvironment feature representations depicted in FIG. 3 are merelyexamples. In fact, feature decays different than those depicted in FIG.3 can readily be determined by interpolating between the depictedenvironment feature representations. Accordingly, it should beunderstood that a particular environment feature may be visuallyaffected by feature decay in a myriad of ways, such that a particularfeature decay (e.g., “partially worn”) refers to a portion of the myriadnumber of visual appearances. Thus, the illustrated example environmentfeature representations and corresponding feature decay descriptions aremerely examples, and should not be taken to limit the spirit and scopeof the disclosure.

Turning now to FIG. 4, a visualization of an example embodimentdescribed herein is illustrated. As depicted, an observed featurerepresentation 402 is captured including an environment feature,specifically lane marker 404. Observed feature representation 402 isassociated with a first feature decay, specifically a first featuredecay indicating the lane marker 404 is partially decayed. In someembodiments, observed feature representation 402 is captured by a singlesensor, for example an image sensor or camera. In some embodiments,observed feature representation is captured by a plurality of sensorsthat cooperate to capture observed feature representation 402.

Observed feature representation data that represents the observedfeature representation 402 is input into a neural network, specificallyGenerative Adversarial Network (“GAN”) 406 as depicted. GAN 406transforms the observed feature representation data to producestandardized feature representation 408. Standardized featurerepresentation 408 includes an environment feature, specifically lanemarker 410. Standardized feature representation 408 approximates mapfeature representation 412, such that the feature decay associated withmap feature representation 412 is approximated by the standardizedfeature representation 408. Accordingly, lane marker 410 is anapproximation of lane marker 414, such that feature decay affecting lanemarker 410 serves to approximate the feature decay affecting lane marker414.

As described above, observed feature representation 402 may beassociated with a first overall context, for example “summer dayconditions, partially worn.” In some embodiments, standardized featurerepresentation 408 may be associated with a second overall context, suchas “sunny summer day, freshly painted.” In some embodiments, the secondoverall context may match a third overall context associated with mapfeature representation 412. Accordingly, GAN 406, or a similargenerative neural network, may be configured to transform an observedfeature representation having any associated overall context into anapproximation of the overall context associated with a map featurerepresentation. In other words, in an example embodiment, for a storedmap feature representation including a freshly painted lane line, acorresponding standardized feature representation output by a trainedGAN, for example, approximates an observed feature representation,including the same lane line but more significantly decayed, as thoughthe observed lane line were freshly painted (as it is in the map featurerepresentation). Accordingly, the trained GAN approximates the overallcontext associated with a stored map feature representation for an inputobserved environment feature in an observed feature representation.

The standardized feature representation may be the best approximatedfeature matching the environment feature depicted in the map featurerepresentation, such that the overall context of the standardizedfeature representation best approximates the overall context of the mapfeature representation. In some embodiments, to increaseinterpretability of an overall context for a human reader, an overallcontext is a string of text or characters, such as in the example above.Alternatively, in some embodiments, an overall context is associatedwith an overall context identifier, such as a unique numeric identifieror unique alphanumeric identifier, that corresponds to a particularoverall context.

Standardized feature representation data that represents thestandardized feature representation 408 is then used to perform acomparison function 416. Specifically, as depicted, comparison function416 compares the standardized feature representation data, representingthe output from the GAN, with map feature representation data,representing the map feature representation 412. The comparison functionidentifies a localized position by comparing the standardized featurerepresentation data and the map feature representation data to find amatch at a particular localized position in the map.

In some embodiments, the GAN 406, or a similar neural network, istrained using two environment feature representation sets: a firstenvironment feature representation set, wherein each environment featurerepresentation in the first environment feature representation set isassociated with a first feature decay, and a second environment featurerepresentation set, wherein each environment feature representation inthe second environment feature representation set is associated with asecond feature decay, different than the first feature decay. In someembodiments, the two collected environment feature representation setsinclude representations of the same environment feature captured at twodifferent times, and thus associated with two distinct feature decays.For example, in some embodiments, the first environment featurerepresentation set is collected during a first drive along a particularroute, and the second environment feature representation set iscollected during a second drive along the particular route. Collectionof a first environment feature representation set may occur haveoccurred at any length of time from collection of a second environmentfeature representation set.

Each feature set may include one or more environment features associatedwith one or more feature decays. For example, a first environmentfeature representation set may include a set of lane lines, capturedduring the spring, that are freshly painted on a road, and a secondenvironment feature representation set may include the same set of lanelines, captured during the following winter, that are slightly worn dueto use and weather effects. A human operator may associate an overallcontext label with one or more, or each, environment featurerepresentation in an environment feature representation set. Forexample, a human operator may associate an overall context label of“freshly painted” with each label in the first environment featurerepresentation set. In some embodiments, each human-readable overallcontext applied by a human operator may represent a unique overallcontext identifier, allowing for both human interpretability and machineinterpretability. In some embodiments, human interpretability of anoverall context is not material. Accordingly, for each environmentfeature representation in an example environment feature representationset, each overall context may be automatically tagged with an overallcontext identifier, such as a unique alphanumeric identifier or uniquenumeric identifier. By automatically tagging one or more overallcontexts, one or more captured environment feature representation setsmay be automatically constructed with overall context labels withoutrequiring human operators.

By providing a standardized feature representation as input to thecomparison function using a neural network, such as GAN 406, theapparatus 200 of an example embodiment is robust in handling changes infeature decay associated with observed feature representations,regardless of the severity in changes in feature decay. Additionally, byproviding a standardized feature representation as input to thecomparison function, the apparatus 200 of an example embodimentautomatically accounts for changes in feature decay without requiringre-parameterization by hand for hand-tuned feature space comparisons ininstances of visual differences between environment featurerepresentations.

Application Ser. No. 16/174,892 entitled “Method and Apparatus forPredicting Feature Space Decay Using Variational Auto-Encoder Networks”,filed on Oct. 30, 2018, which is hereby incorporated by reference in itsentirety, describes a number of example processes for training alocalization neural network for outputting standardized featurerepresentation data representing a standardized feature representation.

In some embodiments, localized position data 418 is produced and outputby the apparatus 200.

As similarly described above with respect to FIG. 3, the particularenvironment feature representations 402, 408, and 412 are merely examplefeature definitions. In other embodiments, the observed featurerepresentation 402, the standardized feature representation 408, and/orthe map feature representation 412 is depicted differently, such asassociated with a different feature decay. Accordingly, use of thedepicted representations is merely by way of example, and such exampledepictions should not be taken to limit the spirit and scope of thedisclosure

FIG. 5 illustrates a flowchart depicting the operations performed, suchas by apparatus 200, according to an example embodiment of the presentinvention. It will be understood that each block of the flowchart andcombination of blocks in the flowchart may be implemented by variousmeans, such as hardware, firmware, processor, circuitry, and/or othercommunication devices associated with execution of software includingone or more computer program instructions. For example, one or more ofthe procedures described above may be embodied by computer programinstructions. In this regard, the computer program instructions whichembody the procedures described above may be stored by a memory, such asmemory 204 of apparatus 200, and executed by a processor, such asprocessor 202 of the apparatus 200. As will be appreciated, any suchcomputer program instructions may be loaded onto a computer or otherprogrammable apparatus (for example, hardware) to produce a machine,such that the resulting computer or other programmable apparatusimplements the functions specified in the flowchart blocks. Thesecomputer program instructions may also be stored in a computer-readablememory that may direct a computer or other programmable apparatus tofunction in a particular manner, such that the instructions stored inthe computer-readable memory produce an article of manufacture theexecution of which implements the function specified in the flowchartblocks. The computer program instructions may also be loaded onto acomputer or other programmable apparatus to cause a series of operationsto be performed on the computer or other programmable apparatus toproduce a computer-implemented process such that the instructions whichexecute on the computer or other programmable apparatus provideoperations for implementing the functions specified in the flowchartblocks.

Accordingly, blocks of the flowchart support combinations of means forperforming the specified functions and combinations of operations forperforming the specified functions for performing the specifiedfunctions. It will also be understood that one or more blocks of theflowchart, and combinations of blocks in the flowchart, can beimplemented by special purpose hardware-based computer systems whichperform the specified functions, or combinations of special purposehardware and computer instructions.

FIG. 5 is a flowchart of the operations performed, such as by apparatus200, for position localization, specifically for position localizationusing a trained neural network. As depicted, at block 502, observedfeature representation data is received. In some embodiments, theapparatus 200 includes means, such as the processor 202, theinput/output circuitry 2006, the communications circuitry 208, a sensor210 or the like, configured to receive the observed featurerepresentation data is received from a sensor or a plurality of sensors.In some embodiments, the observed feature representation data representsraw sensor data, while in other embodiments, the observed featurerepresentation data has undergone at least one pre-processing step, suchthat the observed feature representation data is in a pre-processed dataformat. In some embodiments, the observed feature representation data isdirectly transmitted from one or more sensors in real time.

At block 504, the apparatus 200 includes means, such as the processor202, the neural network localization circuitry 212, or the like,configured to transform the observed feature representation datareceived at block 502 into standardized feature representation datautilizing a trained neural network. In some embodiments, thestandardized feature representation data represents a standardizedfeature representation that approximates a map feature approximationassociated with a particular feature decay. In some embodiments, thetrained neural network is a Generative Adversarial Network (“GAN”).Regardless of its configuration, the neural network of an exampleembodiment is trained using at least two environment featurerepresentation sets: a first environment feature representation set,wherein each environment feature representation in the first environmentfeature representation set is associated with a first feature decay, anda second environment feature representation set, wherein eachenvironment feature representation in the second environment featurerepresentation set is associated with a second feature decay. In someembodiments, the two collected environment feature representation setscorrespond to one another, such that the each set containsrepresentations that include the same environment features at twodifferent times. For example, in some embodiments, the first environmentfeature representation set is collected during a first drive along aparticular route, and the second environment feature representation setis collected during a second drive along the particular route.

In a particular example embodiment, a map feature representationincluding a lane marker may be associated with a feature decay of“freshly painted”, and an observed feature representation including thelane marker may be associated with a different feature decay of“partially worn”. Accordingly, an example embodiment, the trained neuralnetwork may transform observed feature representation data representingthe observed feature representation described above into standardizedfeature representation data that represents a standardized featurerepresentation that includes the lane marker affected by a new,standardized feature decay, which approximates the lane marker at thetime/affected by the feature decay associated with the map featurerepresentation.

At block 506, the apparatus 200 includes means, such as the processor202, the neural network localization circuitry 212, or the like,configured to compare the standardized feature representation data andmap feature representation data. In an example embodiment, thestandardized feature representation data and map feature representationdata are compared utilizing a comparison function implemented, forexample, by the processor 202 or the neural network localizationcircuitry 212. In an example embodiment, the comparison functiondetermines if the standardized feature representation data and the mapfeature representation data are a match. In another example embodiment,the comparison function determines if the standardized featurerepresentation data and the map feature representation data a similarityrelationship that represents a match based on a comparison threshold. Inan example embodiment, the comparison function determines a match byminimizing an error function between the standardized featurerepresentation data and the map feature representation data.

When a match is determined, the apparatus 200 includes means, such asthe processor 202, the neural network localization circuitry 212, or thelike, configured to identify localized position data. In someembodiments, the comparison function identifies and outputs a localizedposition, or corresponding localized position data representing thelocalized position, after determining a match. For example, in anexample embodiment, a GPS position is received and used to perform alocal search within a particular search field. In some embodiments,localized positions in the search field are utilized to determine if thestandardized feature representation data matches the map featurerepresentation data, and, if so, the localized position that bestmatches is identified.

By providing a standardized feature representation as input to thecomparison function using a neural network, the method, apparatus 200,and computer program product of an example embodiment is robust inhandling changes in feature decay associated with observed featurerepresentations, regardless of the severity in changes in feature decay.Additionally, by providing a standardized feature representation asinput to the comparison function, the method, apparatus 200, andcomputer program product of an example embodiment automatically accountsfor changes in feature decay without requiring re-parameterization byhand for hand-tuned feature space comparisons in instances of visualdifferences between environment feature representations.

At optional block 510, the apparatus 200 includes means, such as theprocessor 202, the input/output circuitry 206, the communicationscircuitry 208, or the like, configured to output and/or store, such asin the memory 204, the identified localized position data. In otherembodiments, the localized position data is output to a decision-makingsystem. For example, the localized position data is output to a secondsystem for further processing. In some embodiments, the localizedposition data of an example embodiment is output to a control ornavigation system. For example, the control or navigation system mayreceive the localized position data and, through communication with adecision-making system, alter the steering of an autonomous vehiclebased on the received localized position data to steer towards adestination location (e.g., to stay centered in a current lane), therebyproviding for more accurate and reliable navigation.

In some embodiments, some of the operations above may be modified orfurther amplified. Furthermore, in some embodiments, additional optionaloperations may be included. Modifications, amplifications, or additionsto the operations above may be performed in any order and in anycombination.

CONCLUSION

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims. Forexample, although are described specific embodiments regarding lanemarkers, methods, apparatuses, and computer program products disclosedherein can be used in conjunction with a variety of environment featuressubject to decay over time (e.g., exit signs/street signs obscured byfoliage, crosswalks worn over time, road sign text fading over time, andthe like). Moreover, although the foregoing descriptions and theassociated drawings describe example embodiments in the context ofcertain example combinations of elements and/or functions, it should beappreciated that different combinations of elements and/or functions maybe provided by alternative embodiments without departing from the scopeof the appended claims. In this regard, for example, differentcombinations of elements and/or functions than those explicitlydescribed above are also contemplated as may be set forth in some of theappended claims. Although specific terms are employed herein, they areused in a generic and descriptive sense only and not for purposes oflimitation.

What is claimed is:
 1. An apparatus for position localization comprisingat least one processor and at least one non-transitory memory includingcomputer program code instructions stored thereon, the computer programcode instructions configured to, when executed, cause the apparatus to:receive observed feature representation data captured by a sensorcommunicatively coupled with the apparatus, the observed featurerepresentation data comprising an environment feature associated with anobserved feature decay; using a trained localization neural network,transform the observed feature representation data to standardizedfeature representation data, wherein the standardized featurerepresentation data approximates a map feature representation of theenvironment feature; compare the standardized feature representationdata to map feature representation data that was captured duringconstruction of the map feature representation; and identify a localizedposition for the apparatus based on the comparison.
 2. The apparatusaccording to claim 1, wherein the map feature representation data issubject to a feature decay at a time of capture.
 3. The apparatusaccording to claim 1, wherein the observed feature representation datais in a raw data format.
 4. The apparatus according to claim 1, whereinthe observed feature representation data is in a pre-processed dataformat.
 5. The apparatus according to claim 1, wherein the computerprogram code instructions are further configured to, when executed,cause the apparatus to output the localized position.
 6. The apparatusaccording to claim 1, wherein the trained localization neural network isa trained generative adversarial network.
 7. The apparatus according toclaim 1, wherein an overall context of the standardized representationdata corresponds to the overall context of the map featurerepresentation.
 8. A method for position localization comprising:receiving observed feature representation data captured by a sensor, theobserved feature representation data comprising an environment featureassociated with an observed feature decay; using a trained localizationneural network, transform the observed feature representation data tostandardized feature representation data, wherein the standardizedfeature representation data approximates a map feature representation ofthe environment feature; comparing the standardized featurerepresentation data to map feature representation data that was capturedduring construction of the map feature representation; and identifying alocalized position based upon the comparison.
 9. The method according toclaim 8, wherein the map feature representation data is subject to afeature decay at a time of capture.
 10. The method according to claim 8,wherein the observed feature representation data is in a raw dataformat.
 11. The method according to claim 8, wherein the observedfeature representation data is in a pre-processed data format.
 12. Themethod according to claim 8, further comprising outputting the localizedposition.
 13. The method according to claim 8, wherein the trainedlocalization neural network is a trained generative adversarial network.14. The method according to claim 8, wherein an overall context of thestandardized representation data corresponds to the overall context ofthe map feature representation.
 15. A computer program product forposition localization, the computer program product comprising at leastone non-transitory computer-readable storage medium havingcomputer-executable program code instructions stored therein, thecomputer-executable program code instructions comprising program codeinstructions for: receiving observed feature representation datacaptured by a sensor, the observed feature representation datacomprising an environment feature associated with an observed featuredecay; using a trained localization neural network, transform theobserved feature representation data to standardized featurerepresentation data, wherein the standardized feature representationdata comprises a map feature representation of the environment feature;comparing the standardized feature representation data to map featurerepresentation data that was captured during construction of the mapfeature representation; and identifying a localized position based onthe comparison.
 16. The computer program product according to claim 15,wherein the map feature representation data is subject to a featuredecay at a time of capture.
 17. The computer program product accordingto claim 15, wherein the observed feature representation data is in araw data format.
 18. The computer program product according to claim 15,wherein the observed feature representation data is in a pre-processeddata format.
 19. The computer program product according to claim 15,further comprising program code instructions for outputting thelocalized position.
 20. The computer program product according to claim15, wherein the trained localization neural network is a trainedgenerative adversarial network.